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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/13326


    Title: Gender Classification Using Bayesian Classifier with Local Binary Patch Features
    Authors: 黃仲陵;Huang, Chung-Lin
    Contributors: 資訊多媒體應用學系
    Date: 2012-12
    Issue Date: 2012-11-22 16:57:26 (UTC+8)
    Abstract: In this paper, we proposed a hybrid Bayesian estimation framework to deal with the patch similarity for predicting the gender from the facial images. We used Active Appearance Model (AAM) to align the face image in advance. Images are modeled by the patches around the coordinates of the landmark points. In the training phase, these feature patches are approximated by a pre-trained library. In the inference phase, the choice of feature patch determines the classification decision. We also illustrated a hybrid Bayesian framework to marginalize over the feature patches, and determine the classification decision. A library-image selection manner based on the K-means clustering is introduced.
    Relation: International Computer Symposium 2012
    Appears in Collections:[行動商務與多媒體應用學系] 會議論文

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